SWEET: A Realistic Multiwavelength 3D Simulator for Automotive Perceptive Sensors in Foggy Conditions.

Improving the reliability of automotive perceptive sensors in degraded weather conditions, including fog, is an important issue for road safety and the development of automated driving. Cerema has designed the PAVIN platform reproducing fog and rain conditions to evaluate optical automotive sensor performance under these conditions. In order to increase the variety of scenarios and technologies under test, the use of digital simulation becomes a major asset. The purpose of this paper is to revive the debate around the realism of the various models underlying the numerical methods. The simulation of the radiative transfer equation by Monte Carlo methods and by simplified noise models is examined. The results of this paper show some gaps in foggy scenes between the ray-tracing method, which is considered to be the most realistic, and simple models for contrast evaluation, which can have a particularly strong impact on obstacle detection algorithms.

Analysis of Thermal Imaging Performance under Extreme Foggy Conditions: Applications to Autonomous Driving.

Object detection is recognized as one of the most critical research areas for the perception of self-driving cars. Current vision systems combine visible imaging, LIDAR, and/or RADAR technology, allowing perception of the vehicle's surroundings. However, harsh weather conditions mitigate the performances of these systems. Under these circumstances, thermal imaging becomes the complementary solution to current systems not only because it makes it possible to detect and recognize the environment in the most extreme conditions, but also because thermal images are compatible with detection and recognition algorithms, such as those based on artificial neural networks. In this paper, an analysis of the resilience of thermal sensors in very unfavorable fog conditions is presented. The goal was to study the operational limits, i.e., the very degraded fog situation beyond which a thermal camera becomes unreliable. For the analysis, the mean pixel intensity and the contrast were used as indicators. Results showed that the angle of view (AOV) of a thermal camera is a determining parameter for object detection in foggy conditions. Additionally, results show that cameras with AOVs 18° and 30° are suitable for object detection, even under thick fog conditions (from 13 m meteorological optical range). These results were extended using object detection software, with which it is shown that, for the pedestrian, a detection rate ≥90% was achieved using the images from the 18° and 30° cameras.

Measuring the effect of the rainfall on the windshield in terms of visual performance.

Driving through rain results in reduced visual performance, and car designers have proposed countermeasures in order to reduce the impact of rain on driving performance. In this paper, we propose a methodology dedicated to the quantitative estimation of the loss of visual performance due to the falling rain. We have considered the rain falling on the windshield as the main factor which reduces visual performance in driving. A laboratory experiment was conducted with 40 participants. The reduction of visual performance through rain was considered with respect to two driving tasks: the detection of an object on the road (contrast threshold) and reading a road sign. This experiment was conducted in a laboratory under controlled artificial rain. Two levels of rain intensity were compared, as well as two wiper conditions (new and worn), while the reference condition was without rain. The reference driving situation was night driving. Effects of both the rain level and the wipers characteristics were found, which validates the proposed methodology for the quantitative estimation of rain countermeasures in terms of visual performance.